Optimization of Base Energy Resolution in Hemispherical Deflector Analyzer by using Genetic Algorithm

Year 2020, Volume: 16 Issue: 3, 345 - 349, 29.09.2020

Murat İnce , Nimet Işık

https://doi.org/10.18466/cbayarfbe.681519

Cited By: 1

Abstract

The aim of this study is to demonstrate the Genetic Algorithm (GA) optimization results for energy resolutions of Hemispherical Deflector Analyzer (HDA). The HDAs are designed specifically to distinguish electrons according to their energies. In this context, high energy resolutions are important for the prevention of experimental data loss. In this study, the energy resolution values can be obtained in a short time with the aid of the genetic algorithm implemented in the software. Genetic algorithm (GA) is an effective method developed with artificial intelligence technology. For the first time in this study, analyzer resolution values in the widest range in the literature were calculated by genetic algorithm software. Optimum solutions not only for centric entry HDA but also for paracentric entry Hemispherical Deflector Analyzer (HDA) were obtained by the genetic algorithm.

Keywords

Artificial intelligence, electron optics, electrostatic energy analyzer, genetic algorithm, optimization

References

• 1. Harting E., Read F.H. Electrostatic Lenses; Elsevier: Newyork, 1976.
• 2. Kuyatt, C.E., Simpson, J.A. 1967. Electron monochromator design. Review of Scientific Instruments; 38: 103-111.
• 3. Imhof, R.E., Adams, A. King, G.C. 1976. Energy and time resolution of the 180 degrees hemispherical electrostatic analyzer. Journal of Physics E: Scientific Instruments; 9: 138-142.
• 4. Ballu, Y. 1968. Source d'électrons lents monocinétiques. Revue de Physique Appliquée; 3: 46-52.
• 5. Polaschegg, H.D. 1974. Spherical analyzer with pre-retardation. Applied physics; 4: 63-68.
• 6. Wannberg, B., Sköllermo, A. 1977. Computer optimization of retarding lens systems for ESCA spectrometers. Journal of Electron Spectroscopy and Related Phenomena; 10(1): 45-78.
• 7. Dubé, D., Roy, D., Ballu, Y. 1981. New approach to improve performances of electron spectrometers. Review of Scientific Instruments; 52: 1497-1500.
• 8. Benis, E.P., Zouros, T.J.M. 2000. Improving the energy resolution of a hemispherical spectrograph using a paracentric entry at a non-zero potential. Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment; 440: 462-465.
• 9. Zouros, T.J.M. 2006. Theoretical investigation of the energy resolution of an ideal hemispherical deflector analyzer and its dependence on the distance from the focal plane. Journal of Electron Spectroscopy and Related Phenomena; 152(1-2): 67-77.
• 10. Zouros, T. J. M., Sise, O., Ulu, M., Dogan, M. 2006. Using the fringing fields of a hemispherical spectrograph to improve its energy resolution. Measurement Science and Technology; 17(12): N81.
• 11. Sise, O., Zouros, T.J.M., Ulu, M., Dogan, M. 2007. Novel and traditional fringing field correction schemes for the hemispherical analyzer: comparison of first-order focusing and energy resolution. Measurement Science and Technology; 18(7): 1853.
• 12. Dahl, D.A. 1996. SIMION 3D v7.0 (Idaho Falls: Idaho National Engineering Laboratory).
• 13. Sise O., Ulu M., Dogan M., Martinez G., Zouros T.J. 2010. Fringing field optimization of hemispherical deflector analyzers using BEM and FDM. Journal of Electron Spectroscopy and Related Phenomena; 177: 42-51.
• 14. Goldberg, D.E., Holland, J.H. 1988. Genetic algorithms and machine learning. Machine learning; 3: 95-99.
• 15. Bashir, L.Z., Mahdi, N. 2015. Use Genetic Algorithm in Optimization Function for Solving Queens Problem. World Scientific News; 11: 138-150.
• 16. İnce, M., Yiğit, T., Işık, A.H. 2019. A hybrid AHP-GA method for metadata-based learning object evaluation. Neural Computing and Applications; 31(1): 671-681.
• 17. Ahmadi, M.H., Ahmadi, M.A. 2016. Thermodynamic analysis and optimisation of an irreversible radiative-type heat engine by using non-dominated sorting genetic algorithm. International Journal of Ambient Energy; 37: 403-408.
• 18. Zhang, L., Wang, L., Hinds, G., Lyu, C., Zheng, J., Li, J. 2014. Multi-objective optimization of lithium-ion battery model using genetic algorithm approach. Journal of Power Sources; 270: 367-378.
• 19. Goldberg, D.E., 1989. Genetic algorithms in search optimization and machine learning. Addison Wesley, Reading Menlo Park.
• 20. Abkenar, S.M.S., Stanley, S.D., Miller, C.J., Chase, D.V., McElmurry, S.P. 2015. Evaluation of genetic algorithms using discrete and continuous methods for pump optimization of water distribution systems. Sustainable Computing: Informatics and Systems; 8: 18-23.
• 21. Davis, L. Handbook of genetic algorithms, 1991.
• 22. Srinivas, M., Patnaik, L.M. 1994. Genetic algorithms: A survey. Computer; 27(6): 17-26.
• 23. Rezaie, A., Tsatsaronis, G., Hellwig, U. 2019. Thermal design and optimization of a heat recovery steam generator in a combined-cycle power plant by applying a genetic algorithm. Energy; 168: 346-357.
• 24. Askarzadeh, A., 2018. A memory-based genetic algorithm for optimization of power generation in a microgrid. IEEE Transactions on Sustainable Energy; 9: 1081-1089.
• 25. Downey, A., Hu, C., Laflamme, S. 2018. Optimal sensor placement within a hybrid dense sensor network using an adaptive genetic algorithm with learning gene pool. Structural Health Monitoring; 17: 450-460.
• 26. Armaghani, D.J., Hasanipanah, M., Mahdiyar, A., Majid, M. Z. A., Amnieh, H. B., Tahir, M. M. 2018. Airblast prediction through a hybrid genetic algorithm-ANN model. Neural Computing and Applications; 29: 619-629.
• 27. Ray, P., Panda, S.K., Mishra, D.P. Short-term load forecasting using genetic algorithm. Computational Intelligence in Data Mining; Springer: Singapore, 2019; pp 863.